Source-temporal-features for detection EEG behavior of autism spectrum disorder

Author

Shams, W.K. ; Wahab, Abdul

Author_Institution

Fac. of Inf. & Commun. Technol., Int. Islamic Univ. Malaysia, Kuala Lumpur, Malaysia

fYear

2013

fDate

26-27 March 2013

Firstpage

1

Lastpage

5

Abstract

This study introduces a new model to capture the abnormal brain activity of children with Autism Spectrum Disorder (ASD) during eyes open and eyes closed resting conditions. EEG data was collected from normal subjects´ ages (4 to 9) years and ASD subjects match group. Time Difference of Arrival (TDOA) approach was applied with EEG data raw for feature extracted at time domain. The neural network, Multilayer Perception (MLP) was used to distinguish between the two groups during the two tasks. Results show significant accuracy around 98% for both tasks and clearly discriminate for the features in z-dimension his electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.

Keywords

direction-of-arrival estimation; electroencephalography; feature extraction; medical signal detection; medical signal processing; multilayer perceptrons; time-domain analysis; ASD subject match group; EEG behavior detection; EEG data; MLP; TDOA approach; abnormal brain activity; autism spectrum disorder; electronic document; eye-closed resting condition; eyes open resting condition; feature extraction; multilayer perception; neural network; source-temporal-features; time difference-of-arrival approach; time domain; z-dimension; Autism; Biology; Electroencephalography; ASD; EEG; classification; relative source temporal features;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Communication Technology for the Muslim World (ICT4M), 2013 5th International Conference on

Conference_Location

Rabat

Print_ISBN

978-1-4799-0134-0

Type

conf

DOI

10.1109/ICT4M.2013.6518913

Filename

6518913